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Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning.

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A new computer-aided diagnosis system using ResNet50 achieved 98% accuracy in detecting COVID-19 from X-ray images. This real-time system supports physicians and enhances pandemic response capabilities.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Infectious Disease Diagnostics

Background:

  • The rapid global spread of COVID-19 poses a significant threat to healthcare systems worldwide.
  • Accurate and timely diagnosis of COVID-19 is crucial for patient management and disease control.
  • Chest X-ray imaging offers a rapid diagnostic tool for identifying COVID-19 infections.

Purpose of the Study:

  • To develop and evaluate an online, real-time computer-aided diagnosis (CAD) system for COVID-19 detection using chest X-ray images.
  • To support physicians in making faster and more accurate diagnoses, thereby aiding in the containment of the pandemic.
  • To enhance the efficiency and reliability of diagnostic processes in remote healthcare settings.

Main Methods:

  • Implementation of a convolutional neural network (CNN) model, specifically ResNet50, for analyzing chest X-ray images.
  • Development of a CAD system capable of receiving X-ray images from remote hospitals for real-time diagnostic processing.
  • Integration of advanced load balancing and resilience features to ensure fault tolerance and zero-delay performance.

Main Results:

  • The ResNet50-based CAD system achieved a high accuracy of 98% in detecting COVID-19 from chest X-ray images.
  • The system demonstrated the capability for real-time processing of images from remote healthcare centers.
  • The implemented fault tolerance and load balancing features ensured a robust and efficient diagnostic workflow.

Conclusions:

  • The developed CAD system shows significant promise as an effective tool for the rapid and accurate detection of COVID-19.
  • The system's real-time capabilities and high accuracy can greatly assist healthcare professionals in managing the pandemic.
  • The integration of advanced features ensures the system's reliability and scalability for widespread clinical application.